Language
English
Publication Date
5-10-2024
Journal
Patterns
DOI
10.1016/j.patter.2024.100986
PMID
38800365
PMCID
PMC11117058
PubMedCentral® Posted Date
5-2-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.
Keywords
spatial transcriptomics, multi-sample analysis, cellular deconvolution, gene expression, Bayesian modeling
Published Open-Access
yes
Recommended Citation
Niyakan, Seyednami; Sheng, Jianting; Cao, Yuliang; et al., "MUSTANG: Multi-Sample Spatial Transcriptomics Data Analysis With Cross-Sample Transcriptional Similarity Guidance" (2024). Faculty and Staff Publications. 2078.
https://digitalcommons.library.tmc.edu/baylor_docs/2078
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